Über diese Postdoctoral Fellow Stelle bei Mass General Brigham
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Job Summary
The Reshef Lab is seeking an outstanding postdoctoral researcher with training in computational biology, statistics, machine learning, and/or artificial intelligence. This is a primarily computational position focused on developing novel computational methods and applying them to cutting-edge molecular datasets to address fundamental questions in human autoimmunity.
Qualifications
This is a courtesy posting. Please do not submit your application through the Workday "Apply" button.
To be considered for this position, email your CV, cover letter describing your research interests and goals to Yakir Reshef at [email protected]
Applications submitted only through Workday will not be forwarded to the hiring manager and may not be reviewed.
The successful candidate will have a strong quantitative foundation, demonstrated experience developing or applying computational methods to biological data, and a deep interest in solving challenging problems at the intersection of computation and biology. We are particularly interested in candidates who are excited to develop new approaches rather than simply apply existing tools, and who aspire to lead independent research programs. The candidate will work closely with collaborators across computational biology, genomics, immunology, and rheumatology.
The lab provides an interdisciplinary environment spanning machine learning, statistical methodology, single-cell and spatial genomics, and human immunology. The successful candidate will have access to large, richly characterized human molecular datasets and the opportunity to develop methods with broad relevance to biomedical research while using them to make biological discoveries in autoimmunity.
Additional Job Details (if applicable)
The compensation range for this role is between $71,750 - $81,179. Candidate experience is thoughtfully considered throughout the recruitment process, and those with significant experience may be considered for compensation above the starting range listed, as consistent with organizational guidelines and internal equity
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